Traditional business intelligence technologies have helped the organizations to gain deeper insight from their data. With the help of intuitive charts, KPIs, verified numbers and facts, business can analyze customer choices, business trends, buying behaviors and preferences. Analytics have helped to predict the pattern for the future as well. This is great, thanks to the big data, analytics, and business intelligence technologies.
But, what next?
One key question these technologies were unable to address is:
- Why do customers make those choices
- Why some behaviour patterns are more common than others?
This is where a more qualititative approach can help the business, precisely what thick data is all about.
These are top must read data analytic articles from Harvard Business Review (HBR) based on popularity and content. These articles provide crucial updates on the latest technology and innovations happenings around the world in the field of big data and analytics. Even some of the very old articles are still worth reading.
Disclosure: HBR magazine is subscription based. You can view olny few articles per month for free. For more, you need to pay the subscription amount. BusiTelCe derives no form of benefit if you subscribe to HBR. This article is for information purpose only.
A typical grocery store often sells non-perishable food items that are packaged in cans, bottles and boxes, with some stoes also having fresh produce, butchers, delis, and bakeries. As groceries are mostly commodities, they have very little margin. Branding can help them to increase the margin, but that is not feasible for the most. Even for the ones who wants to increase branding, biggest challnge is to do proper analysis and finding insight in their business.
Can todays analytic technology bridge this gap and help grocery business with such insights? Lets explore.
I have often came across this question - at times as a direct question from few of my coleagues and also at times as a point of discussion while designing business intelligence system for the clients.
Data warehousing is the buzzword for the past two decades and big data is hot trend in the recent decade. Lets find out what could be the answer for this question.
Plethora of study has been done to forecast a stock price using predictive algorithms and other statistical techniques. As a novice in the field of machine learning, I was curious to see to how a stock price can be predicted using multiple regression.
For this, I have pulled some data from nseindia.com and then processed these to suit my needs. This page has the quick summary of my study and findings.